Backtesting extreme value theory models of expected shortfall

We use stock market data to analyze the quality of alternative models and procedures for forecasting expected shortfall (ES) at different significance levels. We compute ES forecasts from conditional models applied to the full distribution of returns as well as from models that focus on tail events...

Descripción completa

Detalles Bibliográficos
Autores: Novales Cinca, Alfonso, García Jorcano, Laura
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/42637
Acceso en línea:https://doi.org/10.1080/14697688.2018.1535182
https://hdl.handle.net/10578/42637
Access Level:acceso embargado
Palabra clave:Backtesting
Expected shortfall
Extreme value theory
Filtered historical simulation
Skewed distributions
id ES_4a8a7327d2e2986c08545c19d91d0353
oai_identifier_str oai:ruidera.uclm.es:10578/42637
network_acronym_str ES
network_name_str España
repository_id_str
spelling Backtesting extreme value theory models of expected shortfallNovales Cinca, AlfonsoGarcía Jorcano, LauraBacktestingExpected shortfallExtreme value theoryFiltered historical simulationSkewed distributionsWe use stock market data to analyze the quality of alternative models and procedures for forecasting expected shortfall (ES) at different significance levels. We compute ES forecasts from conditional models applied to the full distribution of returns as well as from models that focus on tail events using extreme value theory (EVT). We also apply the semiparametric filtered historical simulation (FHS) approach to ES forecasting to obtain 10-day ES forecasts. At the 10-day horizon we combine FHS with EVT. The performance of the different models is assessed using six different ES backtests recently proposed in the literature. Our results suggest that conditional EVT-based models produce more accurate 1-day and 10-day ES forecasts than do non-EVT based models. Under either approach, asymmetric probability distributions for return innovations tend to produce better forecasts. Incorporating EVT in parametric or semiparametric approaches also improves ES forecasting performance. These qualitative results are also valid for the recent crisis period, even though all models then underestimate the level of risk. FHS narrows the range of numerical forecasts obtained from alternative models, thereby reducing model risk. Combining EVT and FHS seems to be best approach for obtaining accurate ES forecasts.Taylor & Francis202520252019info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://doi.org/10.1080/14697688.2018.1535182https://hdl.handle.net/10578/42637reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésinfo:eu-repo/semantics/embargoedAccessoai:ruidera.uclm.es:10578/426372026-05-27T07:36:41Z
dc.title.none.fl_str_mv Backtesting extreme value theory models of expected shortfall
title Backtesting extreme value theory models of expected shortfall
spellingShingle Backtesting extreme value theory models of expected shortfall
Novales Cinca, Alfonso
Backtesting
Expected shortfall
Extreme value theory
Filtered historical simulation
Skewed distributions
title_short Backtesting extreme value theory models of expected shortfall
title_full Backtesting extreme value theory models of expected shortfall
title_fullStr Backtesting extreme value theory models of expected shortfall
title_full_unstemmed Backtesting extreme value theory models of expected shortfall
title_sort Backtesting extreme value theory models of expected shortfall
dc.creator.none.fl_str_mv Novales Cinca, Alfonso
García Jorcano, Laura
author Novales Cinca, Alfonso
author_facet Novales Cinca, Alfonso
García Jorcano, Laura
author_role author
author2 García Jorcano, Laura
author2_role author
dc.subject.none.fl_str_mv Backtesting
Expected shortfall
Extreme value theory
Filtered historical simulation
Skewed distributions
topic Backtesting
Expected shortfall
Extreme value theory
Filtered historical simulation
Skewed distributions
description We use stock market data to analyze the quality of alternative models and procedures for forecasting expected shortfall (ES) at different significance levels. We compute ES forecasts from conditional models applied to the full distribution of returns as well as from models that focus on tail events using extreme value theory (EVT). We also apply the semiparametric filtered historical simulation (FHS) approach to ES forecasting to obtain 10-day ES forecasts. At the 10-day horizon we combine FHS with EVT. The performance of the different models is assessed using six different ES backtests recently proposed in the literature. Our results suggest that conditional EVT-based models produce more accurate 1-day and 10-day ES forecasts than do non-EVT based models. Under either approach, asymmetric probability distributions for return innovations tend to produce better forecasts. Incorporating EVT in parametric or semiparametric approaches also improves ES forecasting performance. These qualitative results are also valid for the recent crisis period, even though all models then underestimate the level of risk. FHS narrows the range of numerical forecasts obtained from alternative models, thereby reducing model risk. Combining EVT and FHS seems to be best approach for obtaining accurate ES forecasts.
publishDate 2019
dc.date.none.fl_str_mv 2019
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.1080/14697688.2018.1535182
https://hdl.handle.net/10578/42637
url https://doi.org/10.1080/14697688.2018.1535182
https://hdl.handle.net/10578/42637
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869407500786204672
score 15,81155

Similares en LA Referencia